Adaptive Beamforming Using a Microphone Array for Hands-Free Telephony

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This thesis describes the design and implementation of a 4-channel microphone array that is an adaptive beamformer used for hands-free telephony in a noisy environment. The microphone signals are amplified, then sent to an A/D converter. The microprocessor board takes the data from the 4 channels and utilizes digital signal processing to determine the direction-of-arrival of the sources and create an output which 'steers' the microphone array to the desired look direction while trying to minimize the energy of interference sources and noise. All of the processing for this thesis will be done on a computer using MATLAB.
The MUSIC algorithm is used for direction finding in this thesis. It is shown to be effective in estimating direction-of-arrival for 1 speech source and 2 speech sources that are spaced fairly apart, with significant results down to a -5 dB SNR even. The MUSIC algorithm requires knowledge of the number of sources a priori, requiring an estimator for the number of sources. Though proposed estimators for the number of sources were examined, an effective estimator was not encountered for the case where there are multiple speech sources.
Beamforming methods are examined which utilize knowledge of the source direction-of-arrival from the MUSIC algorithm. The input is split into 6 subbands such that phase-steered beamforming would be possible. Two methods of phase-steered beamforming are compared in both narrowband and wideband scenarios, and it is shown that phase-steering the array to the desired source direction-of-arrival has about 0.3 dB better beamforming performance than the simple time-delay steered beamformer using no subbands.
As the beamforming solution is inadequate to achieve desired results, a generalized sidelobe canceler (GSC) is developed which incorporates a beamformer. The sidelobe canceler is evaluated using both NLMS and RLS adaptation. The RLS algorithm inherently gives better results than the NLMS algorithm, though the computational complexity renders the solution impractical for implementation with today's technology.
A testing setup is presented which involves a linear 4-microphone array connected to a DSP chip that collects the data. Tests were done using 1 speech source and a model of the car noise environment. The sidelobe canceler's performance using 6 subbands (phase-delay GSC) and using 1 band (time-delay GSC) with NLMS updating are compared. The overall SNR improvement is determined from the signal and noise input and output powers, with signal-only as the input and noise-only as the input to the GSC. The phase-delay GSC gives on average 7.4 dB SNR improvement while the time-delay GSC gives on average 6.2 dB SNR improvement.